CVE-2017-16365 in Acrobat Reader
Summary
by MITRE
An issue was discovered in Adobe Acrobat and Reader: 2017.012.20098 and earlier versions, 2017.011.30066 and earlier versions, 2015.006.30355 and earlier versions, and 11.0.22 and earlier versions. The vulnerability is caused by a buffer over-read in the True Type2 Font parsing module. A corrupted cmap table input leads to a computation where the pointer arithmetic results in a location outside valid memory locations belonging to the buffer. An attack can be used to obtain sensitive information, such as object heap addresses, etc.
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Analysis
by VulDB Data Team • 01/24/2021
The vulnerability identified as CVE-2017-16365 represents a critical buffer over-read flaw within Adobe Acrobat and Reader applications across multiple version ranges including 2017.012.20098 and earlier, 2017.011.30066 and earlier, 2015.006.30355 and earlier, and 11.0.22 and earlier versions. This issue resides within the True Type2 Font parsing module, which is responsible for processing font data within PDF documents. The vulnerability stems from inadequate input validation when processing corrupted cmap tables, which are essential components of font data structures that define character-to-glyph mappings. When maliciously crafted font data is processed, the parsing logic fails to properly bounds-check pointer arithmetic operations, leading to memory access violations that extend beyond legitimate buffer boundaries.
The technical exploitation of this vulnerability occurs through carefully constructed malicious PDF documents containing corrupted cmap table data that triggers the buffer over-read condition. During font parsing, the application performs pointer arithmetic operations based on malformed input data from the cmap table, resulting in calculations that point to memory locations outside the intended buffer boundaries. This over-read condition allows attackers to access adjacent memory regions, potentially exposing sensitive information including heap addresses, stack contents, and other memory layout details. The vulnerability is particularly concerning because it operates at the parsing layer where arbitrary user input can be processed without adequate safeguards, making it a prime target for information disclosure attacks that could aid in more sophisticated exploitation techniques.
The operational impact of CVE-2017-16365 extends beyond simple information disclosure, as the leaked memory addresses can significantly aid attackers in bypassing modern security mitigations such as address space layout randomization and stack canaries. This vulnerability aligns with CWE-125, which describes out-of-bounds read conditions, and represents a classic example of memory safety issues that have been prevalent in font parsing libraries. The attack vector typically involves social engineering campaigns where users are诱导 to open malicious PDF documents containing crafted font data. From an adversarial perspective, this vulnerability maps to ATT&CK technique T1059.007 for execution through PDF-based attacks and T1068 for privilege escalation opportunities that may arise from information disclosure. The vulnerability affects organizations across multiple sectors including finance, healthcare, and government entities that rely heavily on Adobe Acrobat Reader for document processing, making it a significant target for advanced persistent threat actors.
Mitigation strategies for CVE-2017-16365 should prioritize immediate patching of affected Adobe Acrobat and Reader versions, with particular emphasis on updating to the latest security patches released by Adobe. Organizations should implement network-based protections such as PDF content filtering and sandboxing solutions that isolate PDF processing environments from critical system resources. Additionally, user education programs should emphasize the importance of avoiding untrusted PDF documents and maintaining updated software versions. Security teams should monitor for exploitation attempts through network traffic analysis and implement intrusion detection systems that can identify malicious PDF content patterns. The vulnerability demonstrates the importance of robust input validation and bounds checking in font processing libraries, with recommendations for implementing stricter memory safety practices and adopting defensive programming techniques that prevent similar issues in future software development cycles.